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Artificial intelligence has replaced big data this year as the most talked about new set of technologies. As with big data five years ago—behind the hype, the confusion generated by an ill-defined term, and the record funding by VC—we are starting to see emerging investments and practical applications where it matters most—in enterprises.

A new report from Narrative Science, based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI), sheds light on the state-of-AI in enterprises today and in the future: 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. Keep in mind that “AI technologies” is a broad term that includes machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response.

Here are some other key findings of the survey:

26% are currently using AI technologies to automate manual, repetitive tasks, up from 15% in 2015

20% of those who haven’t yet adopted AI cite lack of clarity regarding its value proposition

58% are using predictive analytics

25% are using automated written reporting and communications

25% are using voice recognition and response

38% see predictions on activity related to machines, customers or business health as the most important benefit of an AI solution

27% see automation of manual and repetitive tasks as the most important benefit of an AI solution

95% of those who indicated that they are skilled at using big data to solve business problems or generate insights also use AI technologies, up from 59% in 2015

61% of enterprises with an innovation strategy are applying AI to their data to find previously missed opportunities such as process improvements or new revenue streams

Big data has spawned the current interest and increased investment in artificial intelligence. The availability of large volumes of data—plus new algorithms and more computing power—are behind the recent success of deep learning, finally pulling AI out of its long “winter.” More broadly, the enthusiasm around big data (and the success of data-driven digital natives such as Google and Facebook), has led many enterprises to invest heavily in the collection, storage, and organization of data.

But what is to be done with the data? What is the value of having more data if not in new business insights? To uncover new insights, you need hard-to-find data scientists. Indeed, 59% of the respondents to the survey see the shortage of data science talent as the primary barrier to realizing value from their big data technologies. These companies are now turning to AI technologies to help augment their data science capabilities as partial solution to the talent shortage.

Narrative Science, providing software that transforms data into easy-to-read stories, is one of many startups trying to build a bridge between big data and artificial intelligence, between massive generation and collection of data and developing and applying algorithms to make sense of it.

Gartner has coined a new term—Algorithmic Business—to describe the shift of digital businesses from big data to artificial intelligence. Says Gartner: “It is only when the organization shifts from a focus on big data to ‘big answers’ that value begins to emerge… Algorithms are more essential to the business than data alone. Algorithms define action.”

IDC, another analyst firm (and another coiner of new terms), talks about “Cognitive Services” and predicts they will be embedded in new apps, with the top new investment areas over the next couple of years to be “Contextual Understanding” and “Automated Next Best Action capabilities.” Mastering “cognitive” is a must, says IDC, recommending to enterprises to make machine learning a top priority for 2016—“lots of startups in your industry are already using it to disrupt you.”